the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Five years of GOSAT-2 retrievals with RemoTeC: XCO2 and XCH4 data products with quality filtering by machine learning
Jochen Landgraf
Mari Martinez-Velarte
Mihalis Vrekoussis
Ralf Sussmann
Isamu Morino
Kimberly Strong
Minqiang Zhou
Voltaire A. Velazco
Hirofumi Ohyama
Thorsten Warneke
Frank Hase
Tobias Borsdorff
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The Qinghai–Tibetan Plateau is a key system that impacts the global carbon balance. This study presents the greenhouse gas (GHG) mole fraction measurement campaign in May 2022 at Mt. Qomolangma station, including ground-based remote sensing and in situ measurements. The GHG measurements are carried out in this region for the first time and used for satellite validation.
Evaluation of measurement data – Guide to the expression of uncertainty in measurementissued by the JCGM, the error concept and the uncertainty concept are the same. Arguments in favor of the contrary were found not to be compelling. Neither was any evidence presented that
errorsand
uncertaintiesdefine a different relation between the measured and true values, nor is a Bayesian concept beyond the mere subjective probability referred to.
Cited articles
The Greenhouse Gases Observing Satellite-2 (GOSAT-2) is a satellite dedicated to measuring concentrations of greenhouse gases from space. Since its launch, the increase of CH4 and CO2 concentrations in the atmosphere is clear. The datasets obtained from GOSAT-2 are used in the Copernicus atmospheric services to monitor the climate, in light of the Paris Agreement. Here we present robust datasets of these gases from GOSAT-2, including a novel machine learning approach to data quality filtering.
The Greenhouse Gases Observing Satellite-2 (GOSAT-2) is a satellite dedicated to measuring...